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Edit distance

About: Edit distance is a research topic. Over the lifetime, 2887 publications have been published within this topic receiving 71491 citations.


Papers
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Proceedings ArticleDOI
27 Aug 2011
TL;DR: A variety of metrics are explored to compare the automatic pronunciation methods of three freely-available grapheme-to-phoneme packages on a large dictionary using a novel weighted phonemic substitution matrix constructed from substitution frequencies in a collection of trusted alternate pronunciations.
Abstract: As grapheme-to-phoneme methods proliferate, their careful evaluation becomes increasingly important. This paper explores a variety of metrics to compare the automatic pronunciation methods of three freely-available grapheme-to-phoneme packages on a large dictionary. Two metrics, presented here for the first time, rely upon a novel weighted phonemic substitution matrix constructed from substitution frequencies in a collection of trusted alternate pronunciations. These new metrics are sensitive to the degree of mutability among phonemes. An alignment tool uses this matrix to compare phoneme substitutions between pairs of pronunciations. Index Terms: grapheme-to-phoneme, edit distance, substitution matrix, phonetic distance measures

27 citations

Journal ArticleDOI
TL;DR: A peptide matching approach to the multiple comparison of a set of protein sequences by looking for all the words that are common to q of these sequences, where q is a parameter.

27 citations

Proceedings ArticleDOI
01 Nov 2017
TL;DR: This paper presents the LSDE string representation and its application to handwritten word spotting and shows how such a representation produces a more semantically interpretable retrieval from the user's perspective than other state of the art ones such as PHOC and DCToW.
Abstract: In this paper we present the LSDE string representation and its application to handwritten word spotting LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings We show how such a representation produces a more semantically interpretable retrieval from the user's perspective than other state of the art ones such as PHOC and DCToW We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset

27 citations

Proceedings ArticleDOI
07 Oct 2007
TL;DR: The costs for the insertion, deletion and substitution operations were adapted to make the matching algorithm more sensitive, and the results are encouraging.
Abstract: Dental biometrics is used in forensic dentistry to identify or verify persons based on their dental radiographs. This paper presents a method for human identification based on dental work information. The proposed method works with three main processing steps: segmentation (feature extraction), creation of a dental code, and matching. In the segmentation step, seed points of the dental works are detected by thresholding. The final segmentation is obtained with a snake (active contour) algorithm. The dental code is defined from the position (upper or lower), the size of the dental works, and distance between neighboring dental works. The matching stage is performed with the Edit distance (levenshtein distance). The costs for the insertion, deletion and substitution operations were adapted to make the matching algorithm more sensitive. The method was tested on a database including 68 dental radiographs and the results are encouraging.

27 citations

Book ChapterDOI
17 Jun 2002
TL;DR: An algorithm is proposed for automatic L-system translation that compares randomly generated branching structures with the target structure and Edit distance, which is proposed as a measure of dissimilarity between rooted trees, is extended for the comparison of structures represented in axial trees.
Abstract: L-systems are widely used in the modelling of branching structures and the growth process of biological objects such as plants, nerves and airways in lungs. The derivation of such L-system models involves a lot of hard mental work and time-consuming manual procedures. A method based on genetic algorithms for automating the derivation of L-systems is presented here. The method involves representation of branching structure, translation of L-systems to axial tree architectures, comparison of branching structure and the application of genetic algorithms. Branching structures are represented as axial trees and positional information is considered as an important attribute along with length and angle in the database configuration of branches. An algorithm is proposed for automatic L-system translation that compares randomly generated branching structures with the target structure. Edit distance, which is proposed as a measure of dissimilarity between rooted trees, is extended for the comparison of structures represented in axial trees and positional information is involved in the local cost function. Conventional genetic algorithms and repair mechanics are employed in the search for L-system models having the best fit to observational data.

27 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202339
202296
2021111
2020149
2019145
2018139